An average operator-based PD-type iterative learning control for variable initial state error

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This note studies the effect of variable initial state error in iterative learning control (ILC) systems and proposes a new ILC algorithm based on an average operator. Then, it is shown that, when the proposed algorithm is applied to linear time-invariant (LTI) systems, the effect of the initial state error can be exactly estimated under a specific condition, while the existing algorithms guarantee only the boundness of the error or the convergence from stochastic point of view. To show the validity of the proposed algorithm, a numerical example is given.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2005
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, v.50, no.6, pp.865 - 869

ISSN
0018-9286
DOI
10.1109/TAC.2005.849249
URI
http://hdl.handle.net/10203/88134
Appears in Collection
RIMS Journal Papers
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